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      The role of age in the spreading of COVID-19 across a social network in Bucharest

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          Abstract

          We analyse officially procured data detailing the COVID-19 transmission in Romania’s capital Bucharest between 1st August and 31st October 2020. We apply relational hyperevent models on 19,713 individuals with 13,377 infection ties to determine to what degree the disease spread is affected by age whilst controlling for other covariate and human-to-human transmission network effects. We find that positive cases are more likely to nominate alters of similar age as their sources of infection, thus providing evidence for age homophily. We also show that the relative infection risk is negatively associated with the age of peers, such that the risk of infection increases as the average age of contacts decreases. Additionally, we find that adults between the ages 35 and 44 are pivotal in the transmission of the disease to other age groups. Our results may contribute to better controlling future COVID-19 waves, and they also point to the key age groups which may be essential for vaccination given their prominent role in the transmission of the virus.

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          Most cited references55

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          Emergence of Scaling in Random Networks

          Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            Birds of a Feather: Homophily in Social Networks

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              Age-dependent effects in the transmission and control of COVID-19 epidemics

              The COVID-19 pandemic has shown a markedly low proportion of cases among children1-4. Age disparities in observed cases could be explained by children having lower susceptibility to infection, lower propensity to show clinical symptoms or both. We evaluate these possibilities by fitting an age-structured mathematical model to epidemic data from China, Italy, Japan, Singapore, Canada and South Korea. We estimate that susceptibility to infection in individuals under 20 years of age is approximately half that of adults aged over 20 years, and that clinical symptoms manifest in 21% (95% credible interval: 12-31%) of infections in 10- to 19-year-olds, rising to 69% (57-82%) of infections in people aged over 70 years. Accordingly, we find that interventions aimed at children might have a relatively small impact on reducing SARS-CoV-2 transmission, particularly if the transmissibility of subclinical infections is low. Our age-specific clinical fraction and susceptibility estimates have implications for the expected global burden of COVID-19, as a result of demographic differences across settings. In countries with younger population structures-such as many low-income countries-the expected per capita incidence of clinical cases would be lower than in countries with older population structures, although it is likely that comorbidities in low-income countries will also influence disease severity. Without effective control measures, regions with relatively older populations could see disproportionally more cases of COVID-19, particularly in the later stages of an unmitigated epidemic.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                J Complex Netw
                J Complex Netw
                comnet
                Journal of Complex Networks
                Oxford University Press
                2051-1310
                2051-1329
                August 2021
                07 September 2021
                07 September 2021
                : 9
                : 4
                : cnab026
                Affiliations
                Department of Sociology, University of Bucharest, Bucharest , Panduri 90-92, 050663, Romania
                Department of Computer and Information Science , University of Konstanz, 78457, Konstanz, Germany
                Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160 , 2000 Maribor, Slovenia, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404332, Taiwan, Alma Mater Europaea, Slovenska ulica 17, 2000 Maribor, Slovenia and Complexity Science Hub Vienna, Josefstädterstraße 39, 1080 Vienna, Austria
                Department of Sociology, University of Bucharest , Bucharest, Panduri 90-92, 050663, Romania
                Department of Sociology, University of Bucharest , Bucharest, Panduri 90-92, 050663, Romania
                Department of Sociology, University of Bucharest , Bucharest, Panduri 90-92, 050663, Romania
                Department of Sociology, University of Bucharest , Bucharest, Panduri 90-92, 050663, Romania
                Author notes
                Corresponding author. Email: gabriel.hancean@ 123456sas.unibuc.ro
                Author information
                https://orcid.org/0000-0001-9358-5287
                https://orcid.org/0000-0002-6802-5090
                https://orcid.org/0000-0002-3087-541X
                Article
                cnab026
                10.1093/comnet/cnab026
                8499891
                34642603
                f29a18ba-b840-4e96-b372-adcc3c29b4c5
                © The authors 2021. Published by Oxford University Press. All rights reserved.

                This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                : 20 July 2021
                : 14 August 2021
                : 19 August 2021
                Page count
                Pages: 20
                Funding
                Funded by: Executive Unit for Financing Higher Education, Research;
                Funded by: Deutsche Forschungsgemeinschaft (DFG);
                Award ID: 321869138
                Funded by: Slovenian Research Agency, DOI 10.13039/501100004329;
                Categories
                Article
                AcademicSubjects/SCI00010

                covid-19,age-group transmission,network analysis,relational hyperevent models,romania

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